PT-TDGCN: Pre-Trained Trend-Aware Dynamic Graph Convolutional Network for Traffic Flow Prediction

Accurate traffic flow prediction is vital for intelligent transportation systems, yet strong spatiotemporal coupling and multi-scale dynamics make modelling difficult. Existing methods often rely on static adjacency and short input windows, limiting adaptation to time-varying spatial relations and l...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 25; číslo 21; s. 6709
Hlavní autori:	Yang, Hanqing, Wei, Sen, Wang, Yuanqing
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 03.11.2025
Predmet:	Accuracy Analysis Computational linguistics convolutional trend-aware attention dynamic graph Graphs Language processing Natural language interfaces Neural networks pre-training Semantics Social networks Traffic flow traffic flow prediction Trends convolutional trend-aware attention traffic flow prediction dynamic graph pre-training
ISSN:	1424-8220, 1424-8220
On-line prístup:	Získať plný text
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